Hyper-plane in logistic regression vs linear regression for same number of features
Mm, I understand what you want to say, but I think you're slightly wrong with terminology. If you consider a d-dimensional vector space, people usually a hyper-plane (d - 1)-dimensional, because it's a subspace of lower dimensionality. So for your example d = 2 the logistic regression would fit a 1-dimentional line (which would separate 2 classes) and the linear regression would fit a 2-dimentional plane (for every combination of 2 features the prediction would lay on some plane in 3-D space). So the general rule would be logreg -&gt; (d-1)-dimensional hyperplane in d-dimensional vector space, linear regression -&gt; d-dimensional hyperplane in (d+1)-dimensional vector space.